# AceStack AI

> AI technical interview coaching built around the candidate's role, seniority and target company.

AceStack AI builds the likely technical interview loop, opens a purpose-built workspace for each round, supports the attempt without replacing the candidate's work, reviews the submitted evidence against explicit criteria, updates Readiness by stage and routes the next gap-focused drill.

## Key product mechanisms

- AI Coach gives contextual help inside the current attempt.
- Step-by-step guided solutions teach a repeatable process before mock mode removes the scaffolding.
- AI review evaluates the submitted answer, code or artifact against task-specific and seniority-specific criteria.
- Readiness summarizes evaluated evidence by interview stage; untested areas remain unscored.
- Adaptive routing selects the next drill from the weakest relevant interview signal.

## Public pages

- [Product overview](https://acestack.ai/)
- [Technical interview preparation guide](https://acestack.ai/interview-preparation)
- [Interview preparation field notes](https://acestack.ai/blog)
- [Field notes RSS feed](https://acestack.ai/blog/rss.xml)
- [Interview practice catalog](https://acestack.ai/practice)
- [Plans and pricing](https://acestack.ai/pricing)
- [Detailed product and content guide](https://acestack.ai/llms-full.txt)
- [Privacy policy](https://acestack.ai/privacy)
- [Terms of service](https://acestack.ai/terms)

## Supported preparation

- Seniority: Junior, Middle, Senior and Staff/Lead expectations
- Software engineering: screening, algorithms, platform coding, SQL, system design, low-level design, code review, debugging, behavioral, and engineering cases
- Data and machine learning: analytics, experiments, ML system design, model incidents, and production ML reasoning
- Infrastructure: CI/CD, Kubernetes, infrastructure review, observability, incident response, and on-call leadership

## Current workspace inventory

- Core (5): Voice Screening, Algorithm Coding, Platform Coding, SQL Case, System Design
- ML and data (4): ML Notebook Lab, Experiment Review, ML System Design, Model Incident Simulator
- Infrastructure and reliability (6): Incident Console, CI/CD Debugger, Kubernetes Lab, Infrastructure Review, Observability Case, Incident Command

## Entity graph

- Product: AceStack AI
- Publisher: AceStack AI
- Canonical site: https://acestack.ai/
- Organization identifier: https://acestack.ai/#organization
- Website identifier: https://acestack.ai/#website
- Public task URL pattern: https://acestack.ai/task/{stable-id}

## Public content boundary

Public task pages may expose a task teaser, stage, difficulty, and applicability. Private expected answers, evaluator prompts, rubrics, hidden tests, attempts, recordings, transcripts, readiness, billing data, and account data are not public sources.

## Crawl sources

- [Sitemap index](https://acestack.ai/sitemap.xml)
- [Static-page sitemap](https://acestack.ai/sitemap-static.xml)
- Public task pages listed in task sitemap chunks are canonical public prompt pages.

## Latest field notes

- [A technical interview plan that does not collapse after week one](https://acestack.ai/blog/technical-interview-preparation-plan): Build a practical interview preparation plan around the actual loop, observable skill gaps, and repeated evidence instead of an endless question queue.
- [What a system design interview is actually measuring](https://acestack.ai/blog/system-design-interview-evaluation): Understand the sequence, decisions, diagram evidence, and seniority signals that make a system design answer credible.
- [Correct code is the start of a senior coding interview](https://acestack.ai/blog/senior-coding-interview-beyond-correct-code): Learn which engineering signals distinguish a correct solution from a strong senior-level coding interview performance.